Project Summary The regulation of synaptic changes during learning involves intricate interactions among distinct cell types. Neuromodulators such as dopamine (DA), acetylcholine (ACh), and norepinephrine (NE) are known to modulate learning-related plasticity. However, the precise cellular loci of these neuromodulators at which they regulate synaptic changes during learning are not fully understood. This is largely due to the fact that in vivo studies of neuromodulator contributions to learning often employ lesion, pharmacology and/or whole-animal knockouts, which do not restrict the manipulations to specific cell types. To address this issue, we propose to establish a novel CRISPR-based method to remove targeted genes of interest in a small subset of cortical neurons. The gene editing will be Cre-dependent and therefore can be applied selectively to specific cell types using Cre lines. The modular nature of the method will make it straightforward to target multiple genes in single neurons, enabling double, triple, and quadruple knockouts. The method also allows specific labelling of these sparse knockout neurons with GFP, which allows us to perform longitudinal imaging of synaptic structures of the genetically modified cells in vivo during learning. Using this CRISPR-based sparse knockout and fluorescence labelling method, we will investigate the requirement of various neuromodulator receptor genes in cortical plasticity during learning. This investigation will take advantage of a system in which we have made pioneering observations of cell-type specific synaptic changes during motor learning (Peters et al., Nature 2014; Chen et al., Nature Neuroscience 2015). Specifically, our lab has identified three distinct, cell-type specific synaptic changes in the motor cortex during motor learning, in principal excitatory neurons, somatostatin-expressing inhibitory neurons (SOM-INs), and parvalbumin-expressing inhibitory neurons (PV-INs). These cell-type specific but interrelated plasticity events likely involve parallel signaling pathways, and each of these plasticity events might require a distinct set of neuromodulator receptors in a cell-type specific manner. Therefore, this motor learning paradigm provides an ideal platform to disentangle the diverse potential contributions of neuromodulator signaling in each cell-type and the CRIPR-based sparse knockouts will be pivotal for us to systematically probe cell-autonomous gene functions in vivo. In addition to the proposed application, the established method will be widely applicable to other genes and biological systems in the future.